Machine-learning quantum mechanics: Solving quantum mechanics problems using radial basis function networks

Peiyuan Teng
Phys. Rev. E 98, 033305 – Published 14 September 2018

Abstract

In this article, machine-learning methods are used to solve quantum mechanics problems. The radial basis function network in a discrete basis is used as the variational wave function for the ground state of a quantum system. Variational Monte Carlo (VMC) calculations are carried out for some simple Hamiltonians. The results are in good agreement with theoretical values. The smallest eigenvalue of a Hermitian matrix can also be acquired using VMC calculations. Results are provided to demonstrate that machine-learning techniques are capable of solving quantum mechanical problems.

  • Figure
  • Figure
  • Figure
  • Figure
  • Figure
  • Figure
  • Figure
2 More
  • Received 10 October 2017
  • Revised 15 April 2018

DOI:https://doi.org/10.1103/PhysRevE.98.033305

©2018 American Physical Society

Physics Subject Headings (PhySH)

Interdisciplinary PhysicsNetworks

Authors & Affiliations

Peiyuan Teng*

  • Department of Physics The Ohio State University Columbus, Ohio, 43210, USA

  • *teng.73@osu.edu

Article Text (Subscription Required)

Click to Expand

References (Subscription Required)

Click to Expand
Issue

Vol. 98, Iss. 3 — September 2018

Reuse & Permissions
Access Options
Author publication services for translation and copyediting assistance advertisement

Authorization Required


×
×

Images

×

Sign up to receive regular email alerts from Physical Review E

Log In

Cancel
×

Search


Article Lookup

Paste a citation or DOI

Enter a citation
×